1. Field of the Invention
The present invention relates to a medical image processing device that applies image processing to a medical image obtained by picking up an image of a biological mucous membrane.
2. Description of the Related Art
Observation using an image pickup apparatus such as an endoscope (including a capsule type) apparatus has been widely performed in a medical field. The endoscope apparatus includes, for example, an elongated insertion portion inserted into a body cavity as a living organism and has a configuration and action for picking up, with image pickup means such as a solid-state image pickup device, an image in the body cavity formed by an objective optical system arranged at a distal end portion of the insertion portion, outputting the image as an image pickup signal, and displaying a video of the image in the body cavity on display means such as a monitor on the basis of the image pickup signal.
A user performs observation of, for example, organs in the body cavity on the basis of the video of the image in the body cavity displayed on the display means such as the monitor. The endoscope apparatus is capable of directly picking up an image of a digestive tract mucous membrane. Therefore, the user can comprehensively observe various findings such as a tone of a mucous membrane, a shape of a lesion, and a microscopic structure on a mucous membrane surface (a mucous membrane microstructure), for example.
A large number of diagnostics for classifying and diagnosing conditions of diseases using the findings of the mucous membrane microstructure (a blood vessel, a pit, an epithelium structure, etc.) are proposed targeting various organs such as a large intestine, a stomach, and an esophagus. For example, as one of diagnostics widely used in Japan, there is a pit pattern classification of a large intestine. Further, in recent years, according to spread of an endoscope for narrow band light observation (NBI), examinations of diagnostics for a medical image picked up by the NBI are actively performed.
However, sufficient experience and the like are necessary for understanding and practice of these diagnostics. Therefore, it is difficult to make best use of the diagnostics when determinations are different depending on doctors and is difficult for an inexperienced doctor to make best use of the diagnostics. Therefore, researches and developments are performed concerning computer aided diagnosis (CAD) for providing, through image processing for a medical image, support information such as an estimation result of a condition of a disease by identification and image analysis of a microstructure that should be paid attention to in provision of a quantitative determination scale and diagnosis.
In a mucous membrane microstructure in an endoscopic image, an image of a continuous pattern is picked up in a complicated form. Highly accurate extraction and analysis are difficult with a conventional image analysis method. An image of a pattern of the mucous membrane microstructure to be picked up is different according to a difference in an organ such as a stomach or a large intestine. Further, in the same organ, for example, in the stomach, an image of a pattern is different in a pyloric gland and a fundic gland.
Furthermore, images of mucous membrane microstructures of a blood vessel, an epithelium structure, and the like are two-dimensionally picked up on an endoscopic image. However, as described in Takeshi Yao: Stomach Magnifying Endoscope; PP. 79-87, 2009 (hereinafter referred to as Non-Patent Literature), the mucous membrane microstructures actually assume three-dimensional structures.
Japanese Patent No. 2918162 describes a method of dividing and detecting a small region with a gastric area in a stomach mucous membrane set as a unit region. However, a minimum unit of a target region is the gastric area. The method does not target an analysis of a structure so complicated and small as a range set as one unit in terms of biological histology. Japanese Patent No. 4451460 discloses content for setting a plurality of regions of interest (abbreviated as ROIs) in an endoscopic image, calculating feature values from the respective ROIs, and estimating and discriminating a pit pattern classification. Note that the setting of the ROIs is manually performed.